{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# word2vec_basic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import collections\n",
    "import math\n",
    "import os\n",
    "import random\n",
    "from tempfile import gettempdir\n",
    "import zipfile\n",
    "\n",
    "import numpy as np\n",
    "from six.moves import urllib\n",
    "from six.moves import xrange  # pylint: disable=redefined-builtin\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### step1 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data size 1786003\n"
     ]
    }
   ],
   "source": [
    "def read_data(filename):\n",
    "    \"\"\"Extract the first file enclosed in a zip file as a list of words.\"\"\"\n",
    "    with open(filename,'rb') as f:\n",
    "        data=[]\n",
    "        for line in f:\n",
    "            col=line.decode().strip()\n",
    "            for word in col:\n",
    "                data.append(word)\n",
    "    return data\n",
    "\n",
    "filename='QuanSongCi.txt'\n",
    "vocabulary = read_data(filename)\n",
    "print('Data size', len(vocabulary))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### step2 创建字典并将稀有数据用unk表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common words (+UNK) [['UNK', 1194], ('。', 149620), ('，', 108451), ('、', 19612), ('人', 13607)]\n",
      "Sample data [1502, 1827, 39, 612, 46, 8, 110, 116, 7, 9] ['潘', '阆', '酒', '泉', '子', '（', '十', '之', '一', '）']\n"
     ]
    }
   ],
   "source": [
    "vocabulary_size = 5000\n",
    "\n",
    "def build_dataset(words, n_words):\n",
    "    \"\"\"Process raw inputs into a dataset.\"\"\"\n",
    "    count = [['UNK', -1]]\n",
    "    #取词频最高的前50000个汉字加入到count中 值表示出现的次数\n",
    "    count.extend(collections.Counter(words).most_common(n_words - 1))\n",
    "    dictionary = dict()\n",
    "    #按count顺序创建字典，key是字 value是顺序\n",
    "    for word, _ in count:\n",
    "        dictionary[word] = len(dictionary)\n",
    "    data = list()\n",
    "    unk_count = 0\n",
    "    #返回各个word在dictionary中的索引，如果不在dictionary的话就将其置为0，通过这样可以得到unk的个数\n",
    "    for word in words:\n",
    "        index = dictionary.get(word, 0)\n",
    "        if index == 0:  # dictionary['UNK']\n",
    "            unk_count += 1\n",
    "        data.append(index)\n",
    "    #重置unk的个数\n",
    "    count[0][1] = unk_count\n",
    "    #反转一下字典，索引在前，word在后\n",
    "    reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\n",
    "    return data, count, dictionary, reversed_dictionary\n",
    "\n",
    "# Filling 4 global variables:\n",
    "# data - list of codes (integers from 0 to vocabulary_size-1).\n",
    "#   This is the original text but words are replaced by their codes\n",
    "# count - map of words(strings) to count of occurrences\n",
    "# dictionary - map of words(strings) to their codes(integers)\n",
    "# reverse_dictionary - maps codes(integers) to words(strings)\n",
    "data, count, dictionary, reverse_dictionary = build_dataset(vocabulary,\n",
    "                                                            vocabulary_size)\n",
    "#del vocabulary  # Hint to reduce memory.\n",
    "print('Most common words (+UNK)', count[:5])\n",
    "print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])\n",
    "\n",
    "data_index = 0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### step3 生成训练的batch  skip-gram model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1827 阆 -> 1502 潘\n",
      "1827 阆 -> 39 酒\n",
      "39 酒 -> 1827 阆\n",
      "39 酒 -> 612 泉\n",
      "612 泉 -> 39 酒\n",
      "612 泉 -> 46 子\n",
      "46 子 -> 612 泉\n",
      "46 子 -> 8 （\n"
     ]
    }
   ],
   "source": [
    "def generate_batch(batch_size, num_skips, skip_window):\n",
    "    #skip_window 表示从当前输入词的一端（左边或右边）选取词的数量\n",
    "    #num_skips 它代表着我们从整个窗口中选取多少个不同的词作为我们的output word\n",
    "    global data_index\n",
    "    assert batch_size % num_skips == 0\n",
    "    assert num_skips <= 2 * skip_window\n",
    "    batch = np.ndarray(shape=(batch_size), dtype=np.int32)#生成一个空的batch\n",
    "    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)#空的label\n",
    "    span = 2 * skip_window + 1  # 扫描的窗口，输入词加其前后的skip_window\n",
    "    buffer = collections.deque(maxlen=span)  # 一个跟span等大的空的deque\n",
    "    if data_index + span > len(data):\n",
    "        data_index = 0\n",
    "    buffer.extend(data[data_index:data_index + span])#从data_index到span的长度填入buffer中\n",
    "    data_index += span\n",
    "    for i in range(batch_size // num_skips):\n",
    "        context_words = [w for w in range(span) if w != skip_window]\n",
    "        words_to_use = random.sample(context_words, num_skips)\n",
    "        for j, context_word in enumerate(words_to_use):\n",
    "            batch[i * num_skips + j] = buffer[skip_window]\n",
    "            labels[i * num_skips + j, 0] = buffer[context_word]\n",
    "        if data_index == len(data):\n",
    "            buffer.extend(data[0:span])\n",
    "            data_index = span\n",
    "        else:\n",
    "            buffer.append(data[data_index])\n",
    "            data_index += 1\n",
    "    # Backtrack a little bit to avoid skipping words in the end of a batch\n",
    "    data_index = (data_index + len(data) - span) % len(data)\n",
    "    return batch, labels\n",
    "\n",
    "#测试用的\n",
    "batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)\n",
    "for i in range(8):\n",
    "    print(batch[i], reverse_dictionary[batch[i]], '->', labels[i, 0],reverse_dictionary[labels[i, 0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### step4 创建并且训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "embedding_size = 128  # Dimension of the embedding vector.\n",
    "skip_window = 1  # How many words to consider left and right.\n",
    "num_skips = 2  # How many times to reuse an input to generate a label.\n",
    "num_sampled = 64  # Number of negative examples to sample.\n",
    "\n",
    "# We pick a random validation set to sample nearest neighbors. Here we limit the\n",
    "# validation samples to the words that have a low numeric ID, which by\n",
    "# construction are also the most frequent. These 3 variables are used only for\n",
    "# displaying model accuracy, they don't affect calculation.\n",
    "valid_size = 16  # Random set of words to evaluate similarity on.\n",
    "valid_window = 100  # Only pick dev samples in the head of the distribution.\n",
    "valid_examples = np.random.choice(valid_window, valid_size, replace=False)\n",
    "\n",
    "graph = tf.Graph()\n",
    "\n",
    "with graph.as_default():\n",
    "\n",
    "  # Input data.\n",
    "  with tf.name_scope('inputs'):\n",
    "    train_inputs = tf.placeholder(tf.int32, shape=[batch_size])\n",
    "    train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
    "    valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n",
    "\n",
    "  # Ops and variables pinned to the CPU because of missing GPU implementation\n",
    "  with tf.device('/cpu:0'):\n",
    "    # Look up embeddings for inputs.\n",
    "    with tf.name_scope('embeddings'):\n",
    "      embeddings = tf.Variable(\n",
    "          tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n",
    "      embed = tf.nn.embedding_lookup(embeddings, train_inputs)#通过lookup方式迅速计算输入和权重的计算\n",
    "\n",
    "    # Construct the variables for the NCE loss\n",
    "    with tf.name_scope('weights'):\n",
    "      nce_weights = tf.Variable(\n",
    "          tf.truncated_normal(\n",
    "              [vocabulary_size, embedding_size],\n",
    "              stddev=1.0 / math.sqrt(embedding_size)))\n",
    "    with tf.name_scope('biases'):\n",
    "      nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
    "\n",
    "  # Compute the average NCE loss for the batch.\n",
    "  # tf.nce_loss automatically draws a new sample of the negative labels each\n",
    "  # time we evaluate the loss.\n",
    "  # Explanation of the meaning of NCE loss:\n",
    "  #   http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/\n",
    "  with tf.name_scope('loss'):\n",
    "    loss = tf.reduce_mean(\n",
    "        tf.nn.nce_loss(\n",
    "            weights=nce_weights,\n",
    "            biases=nce_biases,\n",
    "            labels=train_labels,\n",
    "            inputs=embed,\n",
    "            num_sampled=num_sampled,\n",
    "            num_classes=vocabulary_size))\n",
    "\n",
    "  # Add the loss value as a scalar to summary.\n",
    "  tf.summary.scalar('loss', loss)\n",
    "\n",
    "  # Construct the SGD optimizer using a learning rate of 1.0.\n",
    "  with tf.name_scope('optimizer'):\n",
    "    optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)\n",
    "\n",
    "  # Compute the cosine similarity between minibatch examples and all embeddings.\n",
    "  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True))\n",
    "  normalized_embeddings = embeddings / norm\n",
    "  valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings,\n",
    "                                            valid_dataset)\n",
    "  similarity = tf.matmul(\n",
    "      valid_embeddings, normalized_embeddings, transpose_b=True)\n",
    "\n",
    "  # Merge all summaries.\n",
    "  merged = tf.summary.merge_all()\n",
    "\n",
    "  # Add variable initializer.\n",
    "  init = tf.global_variables_initializer()\n",
    "\n",
    "  # Create a saver.\n",
    "#saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### step5 开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Average loss at step  0 :  196.6733856201172\n",
      "Nearest to 水: 鸽, 车, 翅, 尸, 慑, 赫, 归, 傅,\n",
      "Nearest to 自: 画, 坠, 澒, 融, 渴, 贞, 簪, 脐,\n",
      "Nearest to UNK: 庄, 姮, t, 滔, 娶, 斟, 羁, 镝,\n",
      "Nearest to 月: 迁, 童, 但, 蹒, 爨, 畸, 漉, 霈,\n",
      "Nearest to 梅: 肇, 闾, 浴, 股, 榷, 衱, 祝, 轩,\n",
      "Nearest to 楼: 瓞, 拱, 抆, 螃, 翁, 汉, 节, 擎,\n",
      "Nearest to 又: 腮, 诬, 捏, 粲, 亢, 蓼, 朦, 厢,\n",
      "Nearest to 似: 况, 鸯, 汉, 颔, 胃, 徐, 横, 衫,\n",
      "Nearest to 、: 枇, 佗, 希, 畀, 雀, 莳, 普, 撼,\n",
      "Nearest to 更: 面, 低, 萤, 澧, 矗, 蘸, 榄, 湖,\n",
      "Nearest to 为: 付, 蒨, 苕, 郊, 伪, 筮, 缲, 卜,\n",
      "Nearest to 明: 筵, 戈, 珞, 彭, 妻, 赛, 椒, 霏,\n",
      "Nearest to 烟: 凰, 朔, 雠, 椽, , 续, 毁, 擫,\n",
      "Nearest to 年: 呵, 迂, F, 莺, 愫, 棚, 鲜, 账,\n",
      "Nearest to 深: 茎, 释, 杰, 汾, 亮, 做, 姮, 渌,\n",
      "Nearest to 尽: 翳, 粟, 摺, 机, 盱, 院, 阴, 涉,\n",
      "Average loss at step  2000 :  21.85217563056946\n",
      "Average loss at step  4000 :  5.415847548127174\n",
      "Average loss at step  6000 :  4.985394997239113\n",
      "Average loss at step  8000 :  4.796094733595848\n",
      "Average loss at step  10000 :  4.664332656443119\n",
      "Nearest to 水: 鸽, 色, 车, 阔, 庆, 翅,  , 引,\n",
      "Nearest to 自: 观, 簪, 坠, 画, 疑, 记, 糁, 脐,\n",
      "Nearest to UNK: 庄, 姮,  , 斟, 羁, t, 单, 滔,\n",
      "Nearest to 月: 但, 迁, 童,  , 西, 鞚, 郎, 漉,\n",
      "Nearest to 梅: 股, 肇, 浴, 祝, 途, 菊, 否, 雪,\n",
      "Nearest to 楼: 汉, 扫, 擎, 身, 翁, 拱, 阑, 窕,\n",
      "Nearest to 又: 腮, 粲, 持, 雅, 观, 诬, 朦,  ,\n",
      "Nearest to 似: 况, 鸯, 汉, 为, 徐,  , 颔, 烹,\n",
      "Nearest to 、: ，, 。,  , 窣, 涉, 旄, 甲, 希,\n",
      "Nearest to 更: 面, 萤, 低, 砑, 蘸, 麽, 鹧, 逞,\n",
      "Nearest to 为: 付, 蒨,  , 郊, 苕, 探, 卜, 粗,\n",
      "Nearest to 明: 筵, 戈, 妻, 付, 霄, 彭, 吸, 赛,\n",
      "Nearest to 烟: 云, 朔, 凰, 鹤, 豫, 治, 釭, 吏,\n",
      "Nearest to 年: 呵, 经,  , 莺, 时, 宵, 惜, 障,\n",
      "Nearest to 深: 茎, 亮, 做, 释, 汾, 金, 闰, 杰,\n",
      "Nearest to 尽: 翳, 粟, 涉, 阴, 机, 摺, 院, 折,\n",
      "Average loss at step  12000 :  4.625189694046974\n",
      "Average loss at step  14000 :  4.554122003793716\n",
      "Average loss at step  16000 :  4.560261328458786\n",
      "Average loss at step  18000 :  4.564537116885186\n",
      "Average loss at step  20000 :  4.4923193258047105\n",
      "Nearest to 水: 鸽, 色, 车, 阔, 翅, 惬, 庆, 铎,\n",
      "Nearest to 自: 疑,  , 篱, 贞, 脐, 画, 记, 渴,\n",
      "Nearest to UNK: 庄,  , 姮, 斟, t, 羁, 单, 滔,\n",
      "Nearest to 月: 但, 迁, 西, 童,  , 痼, 鞚, 景,\n",
      "Nearest to 梅: 股, 雪, 浴, 途, 菊, 肇, 乾, 否,\n",
      "Nearest to 楼: 汉, 擎, 阑, 村, 身, 肱, 窕, 腔,\n",
      "Nearest to 又: 腮, 粲, 沉, 捏, 悲,  , 颔, 诬,\n",
      "Nearest to 似: 况, 鸯,  , 为, 汉, 颔, 烹, 如,\n",
      "Nearest to 、: ，, 。,  , 窣, ）, 旄, 涉, 希,\n",
      "Nearest to 更: 面, 萤, 麽, 低, 砑, 鹧, 蘸, 逞,\n",
      "Nearest to 为: 付,  , 蒨, 探, 粗, 伪, 似, 眇,\n",
      "Nearest to 明: 筵, 妻, 付, 戈,  , 光, 霄, 晋,\n",
      "Nearest to 烟: 云, 朔, 凰, 治, 豫, 釭, 雾, 鹤,\n",
      "Nearest to 年: 呵, 时,  , 经, 日, 宵, 敌, 棚,\n",
      "Nearest to 深: 汾, 释, 庭, 茎, 辱, 亮, 闰, 做,\n",
      "Nearest to 尽: 翳, 涉, 粟, 机, 隔, 汀, 摺, 螺,\n",
      "Average loss at step  22000 :  4.4393102142810825\n",
      "Average loss at step  24000 :  4.475389211773872\n",
      "Average loss at step  26000 :  4.519258220553398\n",
      "Average loss at step  28000 :  4.443372357726097\n",
      "Average loss at step  30000 :  4.257432101726532\n",
      "Nearest to 水: 鸽, 色, 阔, 铎, 斐, 车, 惬, 翅,\n",
      "Nearest to 自: 疑,  , 贞, 脐, 醇, 记, 渴, 篱,\n",
      "Nearest to UNK:  , 姮, 庄, 斟, 羁, 徊, 滔, t,\n",
      "Nearest to 月: 迁, 但,  , 景, 西, 童, 痼, 宙,\n",
      "Nearest to 梅: 雪, 菊, 股, 途, 红, 浴, 肇, 乾,\n",
      "Nearest to 楼: 汉, 擎, 肱, 阑, 村, 苑, 矗, 城,\n",
      "Nearest to 又: 腮, 沉, 来, 囗, 粲, 颔, 诬,  ,\n",
      "Nearest to 似: 如, 况,  , 鸯, 得, 为, 汉, 颔,\n",
      "Nearest to 、: ，, 。,  , 窣, 旄, ）, 撋, 峣,\n",
      "Nearest to 更: 面, 萤, 麽, 砑, 低, 尘, 逞, 蘸,\n",
      "Nearest to 为:  , 付, 蒨, 眇, 探, 似, 韫, 伪,\n",
      "Nearest to 明: 晓, 光, 筵, 妻, 椒, 霄, 晋, 戈,\n",
      "Nearest to 烟: 云, 朔, 雾, 凰, 治, 釭, 湄, 豫,\n",
      "Nearest to 年: 时, 呵, 经, 岁,  , 宵, 日, 之,\n",
      "Nearest to 深: 汾, 释, 何, 辱, 庭, 闰, 扪, 茎,\n",
      "Nearest to 尽: 翳, 半, 隔, 涉, 姥, 蛱, 却, 粟,\n",
      "Average loss at step  32000 :  4.291993682026863\n",
      "Average loss at step  34000 :  4.303716901302337\n",
      "Average loss at step  36000 :  4.280224666237831\n",
      "Average loss at step  38000 :  4.278613062620163\n",
      "Average loss at step  40000 :  4.287129024147988\n",
      "Nearest to 水: 鸽, 色, 铎, 路, 阔, 惬, 江, 斐,\n",
      "Nearest to 自: 疑, 醇, 记,  , 贞, 脐, 渴, 篱,\n",
      "Nearest to UNK:  , 庄, 姮, 斟, 单, 徊, 赪, 滔,\n",
      "Nearest to 月: 迁, 但, 景,  , 痼, 西, 鞚, 慳,\n",
      "Nearest to 梅: 雪, 菊, 股, 红, 途, 桃, 浴, 春,\n",
      "Nearest to 楼: 肱, 汉, 苑, 阑, 矗, 墙, 城, 移,\n",
      "Nearest to 又: 腮, 沉, 来, 囗, 颔, 粲, 诬, 仍,\n",
      "Nearest to 似: 如, 况, 为,  , 得, 鸯, 政, 讼,\n",
      "Nearest to 、: ，, 。,  , 峣, 旄, 窣, ）, 撋,\n",
      "Nearest to 更: 麽, 萤, 偏, 倩, 砑, 面, 逞, 犹,\n",
      "Nearest to 为:  , 蒨, 付, 似, 与, 眇, 韫, 探,\n",
      "Nearest to 明: 光, 妻, 晓, 皓, 晋, 筵, 糖, 璧,\n",
      "Nearest to 烟: 云, 雾, 朔, 治, 凰, 豫, 釭, 熏,\n",
      "Nearest to 年: 时, 呵, 岁, 经,  , 日, 阻, 宵,\n",
      "Nearest to 深: 汾, 释, 何, 辱, 庭, 扪, 浅, 姮,\n",
      "Nearest to 尽: 半, 却, 隔, 蛱, 翳, 罍, 姥, 汀,\n",
      "Average loss at step  42000 :  4.288764850258827\n",
      "Average loss at step  44000 :  4.340086472272873\n",
      "Average loss at step  46000 :  4.3247131969928745\n",
      "Average loss at step  48000 :  4.3287762793302536\n",
      "Average loss at step  50000 :  4.29267287325859\n",
      "Nearest to 水: 江, 鸽, 路, 铎, 色, 璈, 斐, 惬,\n",
      "Nearest to 自: 疑, 醇, 记, 篱, 贞, 渴,  , 延,\n",
      "Nearest to UNK:  , 庄, 姮, 滔, 单, 徊, 斟, 赪,\n",
      "Nearest to 月: 痼, 迁,  , 但, 景, 日, 镜, 鞚,\n",
      "Nearest to 梅: 雪, 菊, 红, 桃, 股, 乾, 券, 春,\n",
      "Nearest to 楼: 肱, 汉, 墙, 阑, 城, 苑, 台, 殿,\n",
      "Nearest to 又: 来, 沉, 囗, 早, 颔, 腮, 处, 亢,\n",
      "Nearest to 似: 如,  , 为, 得, 片, 政, 况, 讼,\n",
      "Nearest to 、: ，, 。,  , ）, 旄, 窣, 撋, 峣,\n",
      "Nearest to 更: 麽, 倩, 待, 偏, 萤, 湖, 面, 榄,\n",
      "Nearest to 为:  , 与, 付, 蒨, 似, 韫, 敷, 眇,\n",
      "Nearest to 明: 光, 妻, 皓, 晓, 晋, 虺,  , 赛,\n",
      "Nearest to 烟: 云, 雾, 朔, 治, 绿, 娉, 苔, 湄,\n",
      "Nearest to 年: 时, 岁, 呵,  , 经, 日, 敌, 阻,\n",
      "Nearest to 深: 汾, 何, 庭, 释, 辱, 浅, 帝, 扪,\n",
      "Nearest to 尽: 却, 半, 蛱, 翳, 姥, 腊, 罍, 隔,\n",
      "Average loss at step  52000 :  4.324873028039932\n",
      "Average loss at step  54000 :  4.361883231759071\n",
      "Average loss at step  56000 :  4.312848844170571\n",
      "Average loss at step  58000 :  4.166710301518441\n",
      "Average loss at step  60000 :  4.193534781694412\n",
      "Nearest to 水: 江, 路, 鸽, 阔, 溪, 山, 色, 斐,\n",
      "Nearest to 自: 疑, 醇, 便, 记, 篱, 贞,  , 请,\n",
      "Nearest to UNK:  , 庄, 姮, 赪, 单, 滔, 徊, 瑞,\n",
      "Nearest to 月: 景, 日, 镜, 痼, 迁, 赣,  , 但,\n",
      "Nearest to 梅: 雪, 菊, 股, 桃, 红, 券, 嘘, 花,\n",
      "Nearest to 楼: 城, 墙, 肱, 殿, 汉, 矗, 阑, 台,\n",
      "Nearest to 又: 来, 早, 已, 囗, 沉, 也, 颔, 仍,\n",
      "Nearest to 似: 如,  , 为, 见, 讼, 政, 在, 得,\n",
      "Nearest to 、: ，, 。,  , 旄, 窣, ）, 峣, 璐,\n",
      "Nearest to 更: 麽, 偏, 倩, 待, 萤, 忽, 榄, 面,\n",
      "Nearest to 为: 与,  , 蒨, 付, 似, 敷, 眇, 因,\n",
      "Nearest to 明: 晓, 光, 妻, 皓, 晋, 椒, 糖, 璧,\n",
      "Nearest to 烟: 云, 雾, 香, 治, 娉, 朔, 苔, 熏,\n",
      "Nearest to 年: 岁, 时, 呵,  , 经, 阻, 敌, 之,\n",
      "Nearest to 深: 何, 汾, 释, 辱, 浅, 帝, 庭, 浓,\n",
      "Nearest to 尽: 半, 却, 蛱, 向, 隔, 姥, 腊, 断,\n",
      "Average loss at step  62000 :  4.198295893669129\n",
      "Average loss at step  64000 :  4.201602460384369\n",
      "Average loss at step  66000 :  4.195344221949577\n",
      "Average loss at step  68000 :  4.204224127292633\n",
      "Average loss at step  70000 :  4.218530321359634\n",
      "Nearest to 水: 江, 鸽, 路, 铎, 禾, 山, 斐, 阔,\n",
      "Nearest to 自: 疑, 醇, 记, 篱, 渴, 请, 贞, 便,\n",
      "Nearest to UNK:  , 庄, 姮, 赪, 单, 滔, 徊, 瑞,\n",
      "Nearest to 月: 痼, 镜, 景, 迁, 日, 赣, 影, 姆,\n",
      "Nearest to 梅: 雪, 菊, 桃, 红, 股, 券, 春, 花,\n",
      "Nearest to 楼: 肱, 墙, 城, 殿, 阑, 台, 矗, 苑,\n",
      "Nearest to 又: 早, 已, 囗, 来, 沉, 忽, 仍, 也,\n",
      "Nearest to 似: 如, 为,  , 得, 见, 是, 政, 在,\n",
      "Nearest to 、: ，, 。,  , 峣, 旄, ）, 窣, 榻,\n",
      "Nearest to 更: 麽, 偏, 倩, 待, 忽, 又, 斗, 但,\n",
      "Nearest to 为: 与,  , 似, 蒨, 付, 敷, 眇, 韫,\n",
      "Nearest to 明: 皓, 妻, 光, 晓, 虺, 晋, 璧, 湛,\n",
      "Nearest to 烟: 云, 雾, 香, 熏, 娉, 矜, 苔, 朔,\n",
      "Nearest to 年: 岁, 时, 呵,  , 经, 日, 阻, 憩,\n",
      "Nearest to 深: 汾, 浅, 释, 浓, 何, 帝, 笈, 辱,\n",
      "Nearest to 尽: 半, 却, 蛱, 姥, 麽, 捋, 罍, 腊,\n",
      "Average loss at step  72000 :  4.2538224008083345\n",
      "Average loss at step  74000 :  4.255976384043693\n",
      "Average loss at step  76000 :  4.2566425458192825\n",
      "Average loss at step  78000 :  4.23531254184246\n",
      "Average loss at step  80000 :  4.255460647344589\n",
      "Nearest to 水: 江, 鸽, 路, 溪, 播, 璈, 禾, 铎,\n",
      "Nearest to 自: 疑, 记, 醇, 篱, 请, 便, 贞, 渴,\n",
      "Nearest to UNK:  , 庄, 姮, 滔, 赪, 单, 徊, 斟,\n",
      "Nearest to 月: 日, 镜, 痼, 赣, 影, 迁, 景, 照,\n",
      "Nearest to 梅: 雪, 菊, 桃, 券, 股, 红, 嘘, 抃,\n",
      "Nearest to 楼: 墙, 城, 肱, 殿, 台, 阑, 汉, 矗,\n",
      "Nearest to 又: 早, 已, 来, 忽, 也, 囗, 颔, 沉,\n",
      "Nearest to 似: 如, 为, 见, 是, 在, 片, 记,  ,\n",
      "Nearest to 、: ，, 。, 旄, ）, 峣,  , 窣, 迷,\n",
      "Nearest to 更: 待, 倩, 偏, 麽, 又, 最, 也, 忽,\n",
      "Nearest to 为: 与, 似, 付, 眇,  , 敷, 蒨, 因,\n",
      "Nearest to 明: 皓, 光, 妻, 晓, 湛, 虺, 璧, 椒,\n",
      "Nearest to 烟: 云, 雾, 苔, 娉, 晴, 驺, 诘, 香,\n",
      "Nearest to 年: 岁, 时, 呵,  , 经, 敌, 阻, 记,\n",
      "Nearest to 深: 汾, 浅, 何, 释, 辱, 浓, 密, 笈,\n",
      "Nearest to 尽: 却, 半, 麽, 姥, 都, 蛱, 捋, 筛,\n",
      "Average loss at step  82000 :  4.290607229113578\n",
      "Average loss at step  84000 :  4.236771099925042\n",
      "Average loss at step  86000 :  4.127819807529449\n",
      "Average loss at step  88000 :  4.145503235816956\n",
      "Average loss at step  90000 :  4.141845726013184\n",
      "Nearest to 水: 江, 路, 溪, 山, 鸽, 蕖, 禾, 滔,\n",
      "Nearest to 自: 疑, 请, 便, 记, 醇, 篱, 贞, 偏,\n",
      "Nearest to UNK:  , 庄, 赪, 姮, 滔, 单, 瑞, 徊,\n",
      "Nearest to 月: 日, 镜, 赣, 影, 景, 痼, 灯, 照,\n",
      "Nearest to 梅: 菊, 雪, 股, 券, 桃, 嘘, 杏, 花,\n",
      "Nearest to 楼: 城, 殿, 肱, 台, 墙, 矗, 阑, 山,\n",
      "Nearest to 又: 早, 已, 来, 忽, 还, 也, 囗, 沉,\n",
      "Nearest to 似: 如, 为, 在, 是, 见, 记, 片, 讼,\n",
      "Nearest to 、: ，, 。, 峣, 旄, ）, 璐, 窣, 箬,\n",
      "Nearest to 更: 偏, 但, 倩, 待, 麽, 又, 最, 忽,\n",
      "Nearest to 为: 与, 似, 因, 敷, 遣, 借, 共, 眇,\n",
      "Nearest to 明: 皓, 妻, 光, 晓, 虺, 湛, 璧, 流,\n",
      "Nearest to 烟: 云, 雾, 香, 诘, 晴, 苔, 矜, 驺,\n",
      "Nearest to 年: 岁, 时, 呵, 经, 阻, 改, 敌, 曜,\n",
      "Nearest to 深: 何, 汾, 密, 释, 笈, 浅, 帝, 辱,\n",
      "Nearest to 尽: 半, 却, 麽, 蛱, 断, 姥, 捋, 都,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  92000 :  4.147421454906463\n",
      "Average loss at step  94000 :  4.148572305679322\n",
      "Average loss at step  96000 :  4.152420437812805\n",
      "Average loss at step  98000 :  4.175120266795158\n",
      "Average loss at step  100000 :  4.209545341253281\n",
      "Nearest to 水: 江, 路, 溪, 蕖, 鸽, 璈, 禾, 播,\n",
      "Nearest to 自: 疑, 请, 醇, 篱, 记, 便, 贞, 渴,\n",
      "Nearest to UNK: 庄,  , 赪, 滔, 单, 姮, 陀, 徊,\n",
      "Nearest to 月: 日, 痼, 镜, 影, 赣, 景, 姆, 迁,\n",
      "Nearest to 梅: 菊, 雪, 桃, 券, 杏, 股, 红, 嘘,\n",
      "Nearest to 楼: 城, 殿, 墙, 肱, 台, 矗, 阑, 禾,\n",
      "Nearest to 又: 早, 已, 忽, 还, 仍, 也, 来, 囗,\n",
      "Nearest to 似: 如, 为, 在, 是, 见, 讼, 辉, 记,\n",
      "Nearest to 、: ，, 。, ）, 峣, 窣, 旄, 贰, 璐,\n",
      "Nearest to 更: 偏, 但, 待, 倩, 又, 麽, 便, 矗,\n",
      "Nearest to 为: 与, 似, 借, 遣, 敷, 眇, 因, 共,\n",
      "Nearest to 明: 皓, 光, 湛, 妻, 虺, 照, 亮, 晓,\n",
      "Nearest to 烟: 云, 雾, 香, 驺, 晴, 矜, 诘, 苔,\n",
      "Nearest to 年: 岁, 时, 呵, 经, 敌, 改, 阻, 日,\n",
      "Nearest to 深: 汾, 浅, 密, 笈, 浓, 释, 何, 帝,\n",
      "Nearest to 尽: 却, 半, 麽, 都, 捋, 姥, 蛱, 断,\n",
      "Average loss at step  102000 :  4.205838650465012\n",
      "Average loss at step  104000 :  4.215713461756706\n",
      "Average loss at step  106000 :  4.1900269278287885\n",
      "Average loss at step  108000 :  4.210332249403\n",
      "Average loss at step  110000 :  4.247673053145409\n",
      "Nearest to 水: 江, 溪, 鸽, 蕖, 路, 播, 斐, 禾,\n",
      "Nearest to 自: 疑, 请, 醇, 便, 记, 篱, 扬, 供,\n",
      "Nearest to UNK:  , 庄, 赪, 滔, 单, 姮, 鹥, 轨,\n",
      "Nearest to 月: 日, 赣, 镜, 痼, 影, 照, 有, 蟾,\n",
      "Nearest to 梅: 菊, 雪, 券, 桃, 杏, 股, 红, 嘘,\n",
      "Nearest to 楼: 城, 殿, 肱, 墙, 台, 阑, 矗, 汉,\n",
      "Nearest to 又: 早, 已, 忽, 还, 来, 也, 拏, 仍,\n",
      "Nearest to 似: 如, 是, 见, 为, 记, 在, 讼, 姿,\n",
      "Nearest to 、: ，, 。, ）, 旄, 峣, 璐, 撋, 榻,\n",
      "Nearest to 更: 最, 但, 待, 偏, 又, 倩, 便, 麽,\n",
      "Nearest to 为: 与, 似, 敷, 共, 借, 付, 眇, 因,\n",
      "Nearest to 明: 皓, 光, 妻, 虺, 照, 湛, 璧, 晓,\n",
      "Nearest to 烟: 云, 雾, 驺, 涨, 疟, 诘, 晴, 苔,\n",
      "Nearest to 年: 岁, 时, 呵, 日, 敌, 经, 记, 改,\n",
      "Nearest to 深: 浅, 汾, 何, 密, 释, 浓, 笈, 帝,\n",
      "Nearest to 尽: 却, 都, 半, 麽, 姥, 捋, 正, 终,\n",
      "Average loss at step  112000 :  4.186677506566047\n",
      "Average loss at step  114000 :  4.100572798967361\n",
      "Average loss at step  116000 :  4.119897074460983\n",
      "Average loss at step  118000 :  4.099618085384369\n",
      "Average loss at step  120000 :  4.108306257128715\n",
      "Nearest to 水: 江, 溪, 禾, 路, 蕖, 滔, 鸽, 山,\n",
      "Nearest to 自: 疑, 请, 便, 记, 醇, 篱, 纵, 裛,\n",
      "Nearest to UNK: 庄, 赪, 滔, 单, 姮,  , 轨, 陀,\n",
      "Nearest to 月: 日, 镜, 赣, 影, 蟾, 照, 痼, 灯,\n",
      "Nearest to 梅: 菊, 雪, 杏, 桃, 券, 股, 花, 嘘,\n",
      "Nearest to 楼: 城, 殿, 肱, 台, 墙, 矗, 阑, 山,\n",
      "Nearest to 又: 早, 已, 忽, 还, 拏, 也, 仍, 来,\n",
      "Nearest to 似: 如, 为, 是, 在, 记, 见, 做, 负,\n",
      "Nearest to 、: ，, 。, 峣, 旄, 贰, ）, 璐, 窣,\n",
      "Nearest to 更: 但, 偏, 便, 最, 又, 待, 倩, 渐,\n",
      "Nearest to 为: 与, 似, 借, 共, 因, 敷, 遣, 眇,\n",
      "Nearest to 明: 皓, 光, 晓, 照, 妻, 湛, 璧, 流,\n",
      "Nearest to 烟: 云, 雾, 香, 涨, 诘, 驺, 晴, 疟,\n",
      "Nearest to 年: 岁, 时, 呵, 经, 改, 曜, 敌, 娱,\n",
      "Nearest to 深: 密, 浅, 何, 汾, 浓, 笈, 释, 辱,\n",
      "Nearest to 尽: 却, 半, 麽, 都, 捋, 断, 蛱, 姥,\n",
      "Average loss at step  122000 :  4.125981482088566\n",
      "Average loss at step  124000 :  4.116926275849343\n",
      "Average loss at step  126000 :  4.147250465393066\n",
      "Average loss at step  128000 :  4.175642413854599\n",
      "Average loss at step  130000 :  4.171330238223076\n",
      "Nearest to 水: 江, 溪, 蕖, 滔, 渚, 路, 纡, 胄,\n",
      "Nearest to 自: 疑, 请, 记, 醇, 篱, 贞, 便, 裛,\n",
      "Nearest to UNK: 庄, 滔, 赪, 单, 姮, 陀, 瞢, 轨,\n",
      "Nearest to 月: 日, 赣, 痼, 影, 镜, 蟾, 照, 秋,\n",
      "Nearest to 梅: 菊, 雪, 桃, 杏, 券, 股, 红, 荻,\n",
      "Nearest to 楼: 殿, 城, 肱, 台, 墙, 阑, 立, 社,\n",
      "Nearest to 又: 早, 已, 忽, 还, 仍, 来, 拏, 也,\n",
      "Nearest to 似: 如, 为, 是, 记, 在, 做, 见, 姿,\n",
      "Nearest to 、: ，, 。, 峣, ）, 旄, 墀, 儋, 俱,\n",
      "Nearest to 更: 但, 待, 又, 便, 倩, 偏, 最, 渐,\n",
      "Nearest to 为: 与, 似, 借, 共, 因, 敷, 遣, 贫,\n",
      "Nearest to 明: 皓, 光, 照, 湛, 虺, 妻, 十, 璧,\n",
      "Nearest to 烟: 云, 雾, 驺, 涨, 外, 骤, 香, 诘,\n",
      "Nearest to 年: 岁, 时, 呵, 改, 敌, 记, 日, 憩,\n",
      "Nearest to 深: 密, 浅, 浓, 汾, 笈, 何, 释, 杪,\n",
      "Nearest to 尽: 却, 半, 麽, 都, 断, 捋, 共, 便,\n",
      "Average loss at step  132000 :  4.187676832675934\n",
      "Average loss at step  134000 :  4.1643191868066785\n",
      "Average loss at step  136000 :  4.174944037675858\n",
      "Average loss at step  138000 :  4.214026270508766\n",
      "Average loss at step  140000 :  4.151498307347298\n",
      "Nearest to 水: 江, 溪, 山, 蕖, 滔, 鸽, 渚, 禾,\n",
      "Nearest to 自: 疑, 请, 记, 便, 醇, 供, 篱, 纵,\n",
      "Nearest to UNK: 赪, 庄, 滔, 单, 轨, 秉, 鹥, 姮,\n",
      "Nearest to 月: 日, 照, 赣, 痼, 影, 蟾, 镜, 有,\n",
      "Nearest to 梅: 菊, 雪, 杏, 桃, 券, 股, 梨, 荻,\n",
      "Nearest to 楼: 城, 殿, 肱, 台, 墙, 阑, 层, 山,\n",
      "Nearest to 又: 早, 已, 还, 忽, 拏, 来, 也, 匆,\n",
      "Nearest to 似: 如, 是, 见, 记, 为, 在, 姿, 负,\n",
      "Nearest to 、: ，, 。, ）, 旄, 撋, 璐, 儋, 峣,\n",
      "Nearest to 更: 但, 最, 便, 待, 倩, 偏, 又, 共,\n",
      "Nearest to 为: 与, 借, 似, 共, 敷, 因, 遣, 付,\n",
      "Nearest to 明: 皓, 光, 晓, 璧, 湛, 照, 虺, 莱,\n",
      "Nearest to 烟: 雾, 云, 涨, 疟, 诘, 驺, 香, 骤,\n",
      "Nearest to 年: 岁, 时, 记, 纡, 呵, 仰, 敌, 焉,\n",
      "Nearest to 深: 密, 浅, 何, 汾, 浓, 释, 笈, 院,\n",
      "Nearest to 尽: 却, 都, 麽, 半, 便, 捋, 了, 断,\n",
      "Average loss at step  142000 :  4.074598727703094\n",
      "Average loss at step  144000 :  4.098384988665581\n",
      "Average loss at step  146000 :  4.074298233151436\n",
      "Average loss at step  148000 :  4.08121921736002\n",
      "Average loss at step  150000 :  4.101901133447885\n",
      "Nearest to 水: 江, 溪, 滔, 蕖, 路, 山, 渚, 禾,\n",
      "Nearest to 自: 疑, 请, 记, 便, 醇, 纵, 讫, 扬,\n",
      "Nearest to UNK: 赪, 庄, 滔, 单, 秉, 陀, 闹, 姮,\n",
      "Nearest to 月: 蟾, 日, 赣, 影, 镜, 痼, 照, 灯,\n",
      "Nearest to 梅: 菊, 杏, 桃, 雪, 券, 股, 荻, 梨,\n",
      "Nearest to 楼: 城, 殿, 台, 肱, 墙, 阑, 矗, 层,\n",
      "Nearest to 又: 早, 已, 忽, 还, 拏, 仍, 况, 也,\n",
      "Nearest to 似: 如, 是, 为, 在, 记, 做, 见, 负,\n",
      "Nearest to 、: ，, 。, 峣, 旄, 儋, 璐, ）, 窣,\n",
      "Nearest to 更: 但, 便, 最, 偏, 又, 倩, 待, 渐,\n",
      "Nearest to 为: 与, 似, 借, 因, 共, 遣, 敷, 送,\n",
      "Nearest to 明: 皓, 光, 照, 晓, 湛, 璧, 虺, 妻,\n",
      "Nearest to 烟: 雾, 云, 驺, 涨, 香, 诘, 远, 疟,\n",
      "Nearest to 年: 岁, 时, 记, 呵, 经, 改, 憩, 增,\n",
      "Nearest to 深: 密, 浅, 浓, 何, 汾, 笈, 磻, 杪,\n",
      "Nearest to 尽: 却, 半, 麽, 都, 捋, 断, 姥, 便,\n",
      "Average loss at step  152000 :  4.0856621774435045\n",
      "Average loss at step  154000 :  4.12447106218338\n",
      "Average loss at step  156000 :  4.14826405608654\n",
      "Average loss at step  158000 :  4.15115778696537\n",
      "Average loss at step  160000 :  4.166143806457519\n",
      "Nearest to 水: 江, 溪, 蕖, 渚, 滔, 山, 路, 胄,\n",
      "Nearest to 自: 疑, 记, 请, 醇, 便, 篱, 扬, 钥,\n",
      "Nearest to UNK: 庄, 赪, 滔, 单, 秉, 闹, 陀, 姮,\n",
      "Nearest to 月: 日, 影, 赣, 蟾, 痼, 镜, 照, 秋,\n",
      "Nearest to 梅: 菊, 杏, 雪, 桃, 券, 荻, 股, 抃,\n",
      "Nearest to 楼: 城, 殿, 台, 肱, 但, 墙, 阑, 峰,\n",
      "Nearest to 又: 早, 已, 忽, 还, 拏, 更, 仍, 况,\n",
      "Nearest to 似: 如, 为, 记, 是, 在, 做, 姿, 住,\n",
      "Nearest to 、: ，, 。, ）, 峣, 旄, 儋, 迷, 移,\n",
      "Nearest to 更: 但, 便, 又, 最, 待, 偏, 倩, 渐,\n",
      "Nearest to 为: 与, 似, 共, 借, 因, 遣, 诜, 送,\n",
      "Nearest to 明: 皓, 光, 湛, 虺, 照, 璧, 妻, 杲,\n",
      "Nearest to 烟: 雾, 云, 驺, 涨, 香, 外, 骤, 诘,\n",
      "Nearest to 年: 岁, 时, 日, 敌, 记, 改, 顷, 增,\n",
      "Nearest to 深: 密, 浅, 浓, 笈, 藏, 汾, 何, 杪,\n",
      "Nearest to 尽: 却, 都, 麽, 半, 便, 了, 共, 捋,\n",
      "Average loss at step  162000 :  4.142258355259895\n",
      "Average loss at step  164000 :  4.153333341479302\n",
      "Average loss at step  166000 :  4.182242624402046\n",
      "Average loss at step  168000 :  4.125456815958023\n",
      "Average loss at step  170000 :  4.050024466633797\n",
      "Nearest to 水: 江, 溪, 滔, 山, 波, 蕖, 渚, 箭,\n",
      "Nearest to 自: 疑, 请, 便, 记, 扬, 醇, 供, 讫,\n",
      "Nearest to UNK: 赪, 庄, 秉, 滔, 闹, 单, 轨, 姮,\n",
      "Nearest to 月: 日, 蟾, 影, 赣, 照, 镜, 有, 灯,\n",
      "Nearest to 梅: 菊, 杏, 雪, 桃, 券, 股, 荻, 花,\n",
      "Nearest to 楼: 城, 殿, 肱, 台, 但, 墙, 阑, 峰,\n",
      "Nearest to 又: 早, 已, 还, 忽, 拏, 匆, 也, 仍,\n",
      "Nearest to 似: 如, 是, 为, 记, 在, 见, 疑, 姿,\n",
      "Nearest to 、: ，, 。, 璐, 旄, ）, 峣, 花, 儋,\n",
      "Nearest to 更: 但, 便, 最, 共, 又, 偏, 待, 且,\n",
      "Nearest to 为: 与, 借, 似, 因, 共, 敷, 输, 遣,\n",
      "Nearest to 明: 皓, 光, 晓, 湛, 照, 璧, 虺, 流,\n",
      "Nearest to 烟: 雾, 云, 涨, 香, 驺, 骤, 疟, 湄,\n",
      "Nearest to 年: 岁, 时, 改, 们, 日, 纡, 记, 呵,\n",
      "Nearest to 深: 密, 浅, 浓, 何, 杪, 汾, 院, 藏,\n",
      "Nearest to 尽: 却, 麽, 了, 都, 半, 便, 断, 共,\n",
      "Average loss at step  172000 :  4.08496706700325\n",
      "Average loss at step  174000 :  4.046109512805939\n",
      "Average loss at step  176000 :  4.0653743425607685\n",
      "Average loss at step  178000 :  4.088130099922418\n",
      "Average loss at step  180000 :  4.067013849496841\n",
      "Nearest to 水: 江, 溪, 滔, 脊, 蕖, 渚, 禾, 曦,\n",
      "Nearest to 自: 疑, 请, 便, 记, 醇, 裛, 讫, 扬,\n",
      "Nearest to UNK: 赪, 庄, 秉, 滔, 单, 闹, 陀, 瞢,\n",
      "Nearest to 月: 蟾, 赣, 日, 影, 灯, 痼, 照, 疟,\n",
      "Nearest to 梅: 菊, 杏, 雪, 桃, 荻, 券, 股, 花,\n",
      "Nearest to 楼: 城, 殿, 台, 肱, 峰, 但, 阑, 层,\n",
      "Nearest to 又: 早, 已, 还, 忽, 拏, 仍, 匆, 况,\n",
      "Nearest to 似: 如, 记, 是, 为, 在, 做, 见, 扦,\n",
      "Nearest to 、: ，, 。, 峣, ）, 旄, 贰, 儋, □,\n",
      "Nearest to 更: 但, 便, 最, 偏, 又, 共, 渐, 倩,\n",
      "Nearest to 为: 与, 借, 似, 因, 共, 送, 遣, 同,\n",
      "Nearest to 明: 皓, 光, 照, 湛, 虺, 璧, 晓, 流,\n",
      "Nearest to 烟: 雾, 云, 驺, 骤, 涨, 香, 疟, 湄,\n",
      "Nearest to 年: 岁, 时, 日, 记, 纡, 焉, 改, 呵,\n",
      "Nearest to 深: 密, 浅, 浓, 藏, 磻, 笈, 杪, 汾,\n",
      "Nearest to 尽: 却, 麽, 都, 了, 便, 半, 捋, 冲,\n",
      "Average loss at step  182000 :  4.0973584773540495\n",
      "Average loss at step  184000 :  4.140239978075027\n",
      "Average loss at step  186000 :  4.130315562605857\n",
      "Average loss at step  188000 :  4.1390252956151965\n",
      "Average loss at step  190000 :  4.123300518393517\n",
      "Nearest to 水: 江, 溪, 滔, 蕖, 渚, 萤, 波, 禾,\n",
      "Nearest to 自: 疑, 记, 请, 供, 便, 醇, 扬, 钥,\n",
      "Nearest to UNK: 秉, 赪, 庄, 单, 闹, 滔, 瞢, 轨,\n",
      "Nearest to 月: 蟾, 影, 日, 赣, 痼, 镜, 照, 雪,\n",
      "Nearest to 梅: 菊, 杏, 桃, 雪, 荻, 券, 股, 骢,\n",
      "Nearest to 楼: 城, 殿, 台, 肱, 但, 阑, 峰, 墙,\n",
      "Nearest to 又: 早, 已, 忽, 还, 拏, 况, 匆, 更,\n",
      "Nearest to 似: 如, 记, 是, 为, 在, 奈, 姿, 见,\n",
      "Nearest to 、: ，, 。, ）, 儋, 璐, 说, 旄, 迷,\n",
      "Nearest to 更: 便, 最, 但, 又, 共, 偏, 倩, 待,\n",
      "Nearest to 为: 与, 借, 似, 共, 同, 贫, 因, 送,\n",
      "Nearest to 明: 皓, 光, 照, 湛, 璧, 虺, 莱, 辉,\n",
      "Nearest to 烟: 雾, 云, 涨, 驺, 骤, 诘, 外, 疟,\n",
      "Nearest to 年: 岁, 时, 日, 增, 顷, 纡, 记, 改,\n",
      "Nearest to 深: 密, 浅, 浓, 藏, 何, 杪, 隘, 静,\n",
      "Nearest to 尽: 却, 了, 都, 麽, 便, 半, 捋, 共,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  192000 :  4.139997131586075\n",
      "Average loss at step  194000 :  4.1627429696321485\n",
      "Average loss at step  196000 :  4.096591180562973\n",
      "Average loss at step  198000 :  4.033284740447998\n",
      "Average loss at step  200000 :  4.082186992645264\n",
      "Nearest to 水: 江, 溪, 蕖, 滔, 山, 渚, 箭, 波,\n",
      "Nearest to 自: 疑, 请, 便, 扬, 记, 醇, 正, 讫,\n",
      "Nearest to UNK: 赪, 庄, 秉, 闹, 润, 刍, 滔, 单,\n",
      "Nearest to 月: 蟾, 日, 赣, 影, 灯, 镜, 照, 有,\n",
      "Nearest to 梅: 菊, 杏, 雪, 桃, 荻, 券, 股, 骢,\n",
      "Nearest to 楼: 城, 殿, 台, 肱, 但, 阑, 墙, 峰,\n",
      "Nearest to 又: 早, 已, 还, 拏, 忽, 匆, 况, 也,\n",
      "Nearest to 似: 如, 为, 是, 记, 在, 奈, 见, 疑,\n",
      "Nearest to 、: ，, 。, ）, 峣, 璐, 儋, 旄, 花,\n",
      "Nearest to 更: 便, 但, 最, 又, 共, 偏, 奈, 豚,\n",
      "Nearest to 为: 与, 借, 似, 因, 共, 遣, 贫, 敷,\n",
      "Nearest to 明: 皓, 光, 晓, 照, 湛, 真, 虺, 璧,\n",
      "Nearest to 烟: 雾, 云, 驺, 香, 诘, 涨, 湄, 疟,\n",
      "Nearest to 年: 岁, 时, 记, 增, 泡, 纡, 稀, 日,\n",
      "Nearest to 深: 密, 浅, 浓, 何, 静, 藏, 杪, 汾,\n",
      "Nearest to 尽: 却, 了, 麽, 半, 便, 断, 都, 共,\n",
      "Average loss at step  202000 :  4.019668810009956\n",
      "Average loss at step  204000 :  4.050163414359092\n",
      "Average loss at step  206000 :  4.060794043958187\n",
      "Average loss at step  208000 :  4.053375250697136\n",
      "Average loss at step  210000 :  4.079500596523285\n",
      "Nearest to 水: 江, 溪, 滔, 渚, 蕖, 山, 堠, 脊,\n",
      "Nearest to 自: 疑, 请, 记, 便, 醇, 扬, 正, 钥,\n",
      "Nearest to UNK: 秉, 赪, 闹, 单, 滔, 庄, 刍, 润,\n",
      "Nearest to 月: 蟾, 赣, 日, 影, 灯, , 秋, 痼,\n",
      "Nearest to 梅: 菊, 杏, 桃, 雪, 荻, 梨, 楼, 花,\n",
      "Nearest to 楼: 城, 殿, 台, 阑, 肱, 峰, 但, 山,\n",
      "Nearest to 又: 早, 已, 还, 忽, 拏, 匆, 况, 仍,\n",
      "Nearest to 似: 如, 为, 记, 是, 在, 做, 倩, 奈,\n",
      "Nearest to 、: ，, 。, ）, 峣, 旄, 儋, 添, 过,\n",
      "Nearest to 更: 便, 但, 最, 偏, 又, 共, 渐, 奈,\n",
      "Nearest to 为: 与, 似, 借, 共, 同, 遣, 因, 送,\n",
      "Nearest to 明: 皓, 光, 湛, 照, 虺, 十, 辉, 杲,\n",
      "Nearest to 烟: 雾, 云, 香, 驺, 骤, 涨, 冻, 外,\n",
      "Nearest to 年: 岁, 时, 日, 增, 记, 憩, 改, 们,\n",
      "Nearest to 深: 密, 浅, 浓, 藏, 静, 期, 何, 笈,\n",
      "Nearest to 尽: 却, 了, 麽, 都, 便, 共, 捋, 半,\n",
      "Average loss at step  212000 :  4.12762227010727\n",
      "Average loss at step  214000 :  4.11408897960186\n",
      "Average loss at step  216000 :  4.121181073188782\n",
      "Average loss at step  218000 :  4.101144435405732\n",
      "Average loss at step  220000 :  4.123990314722061\n",
      "Nearest to 水: 江, 溪, 滔, 蕖, 渚, 脊, 鸽, 堠,\n",
      "Nearest to 自: 请, 疑, 醇, 记, 便, 扬, 正, 供,\n",
      "Nearest to UNK: 秉, 赪, 庄, 闹, 滔, 刍, 瞢, 冤,\n",
      "Nearest to 月: 蟾, 赣, 日, 影, 灯, 截, 镜, 顷,\n",
      "Nearest to 梅: 菊, 杏, 桃, 雪, 荻, 券, 梨, 骢,\n",
      "Nearest to 楼: 城, 殿, 台, 但, 肱, 阑, 层, 峰,\n",
      "Nearest to 又: 早, 已, 忽, 还, 拏, 更, 复, 况,\n",
      "Nearest to 似: 如, 是, 为, 记, 见, 倩, 在, 姿,\n",
      "Nearest to 、: ，, 。, ）, 旄, 儋, 璐, 峣, 于,\n",
      "Nearest to 更: 便, 最, 但, 又, 共, 偏, 待, 奈,\n",
      "Nearest to 为: 与, 似, 借, 同, 因, 共, 贫, 遣,\n",
      "Nearest to 明: 皓, 光, 湛, 照, 虺, 辉, 杲, 璧,\n",
      "Nearest to 烟: 雾, 云, 驺, 骤, 涨, 冻, 外, 疟,\n",
      "Nearest to 年: 岁, 时, 日, 记, 增, 凑, 顷, 纡,\n",
      "Nearest to 深: 密, 浅, 浓, 藏, 隘, 静, 期, 杪,\n",
      "Nearest to 尽: 却, 了, 都, 便, 麽, 捋, 总, 贳,\n",
      "Average loss at step  222000 :  4.144684132814407\n",
      "Average loss at step  224000 :  4.0744369351863865\n",
      "Average loss at step  226000 :  4.024426453232765\n",
      "Average loss at step  228000 :  4.062118457198143\n",
      "Average loss at step  230000 :  4.0094921170473095\n",
      "Nearest to 水: 江, 溪, 滔, 山, 渚, 蕖, 脊, 禾,\n",
      "Nearest to 自: 请, 疑, 便, 扬, 记, 裛, 醇, 讫,\n",
      "Nearest to UNK: 秉, 赪, 闹, 庄, 刍, 润, 轨, 暾,\n",
      "Nearest to 月: 蟾, 日, 赣, 影, 灯, 镜, 照, 疟,\n",
      "Nearest to 梅: 菊, 杏, 桃, 雪, 花, 券, 荻, 梨,\n",
      "Nearest to 楼: 城, 台, 殿, 但, 阑, 肱, 峰, 望,\n",
      "Nearest to 又: 早, 已, 还, 拏, 忽, 匆, 怕, 复,\n",
      "Nearest to 似: 如, 是, 为, 在, 记, 与, 做, 见,\n",
      "Nearest to 、: ，, 。, 璐, ）, 峣, 旄, 撋, 贰,\n",
      "Nearest to 更: 便, 但, 最, 共, 渐, 偏, 又, 奈,\n",
      "Nearest to 为: 与, 似, 借, 因, 共, 遣, 同, 贫,\n",
      "Nearest to 明: 皓, 光, 虺, 晓, 湛, 照, 缺, 流,\n",
      "Nearest to 烟: 雾, 云, 驺, 香, 涨, 远, 湄, 诘,\n",
      "Nearest to 年: 岁, 时, 日, 增, 记, 改, 泡, 娱,\n",
      "Nearest to 深: 密, 浓, 静, 何, 浅, 藏, 杪, 汾,\n",
      "Nearest to 尽: 却, 了, 麽, 共, 便, 都, 半, 断,\n",
      "Average loss at step  232000 :  4.039033311724663\n",
      "Average loss at step  234000 :  4.05057847315073\n",
      "Average loss at step  236000 :  4.0347944701910015\n",
      "Average loss at step  238000 :  4.063041743516922\n",
      "Average loss at step  240000 :  4.1299659626483916\n",
      "Nearest to 水: 江, 滔, 溪, 渚, 堠, 蕖, 脊, 曦,\n",
      "Nearest to 自: 请, 疑, 醇, 扬, 正, 记, 钥, 贞,\n",
      "Nearest to UNK: 秉, 赪, 闹, 幰, 冤, 刍, 滔, 陀,\n",
      "Nearest to 月: 蟾, 赣, 日, 影, 痼, 秋, 灯, 疟,\n",
      "Nearest to 梅: 菊, 杏, 桃, 荻, 雪, 梨, 楼, 券,\n",
      "Nearest to 楼: 城, 阑, 殿, 峰, 台, 但, 望, 肱,\n",
      "Nearest to 又: 早, 已, 忽, 拏, 还, 复, 匆, 仍,\n",
      "Nearest to 似: 如, 为, 记, 是, 做, 在, 胜, 奈,\n",
      "Nearest to 、: ，, 。, ）, 峣, 儋, 旄, 皆, 添,\n",
      "Nearest to 更: 便, 但, 最, 又, 偏, 奈, 共, 渐,\n",
      "Nearest to 为: 与, 似, 借, 遣, 因, 同, 贫, 共,\n",
      "Nearest to 明: 皓, 光, 湛, 照, 虺, 辉, 杲, 玷,\n",
      "Nearest to 烟: 雾, 云, 驺, 骤, 湄, 香, 冻, 冉,\n",
      "Nearest to 年: 岁, 时, 日, 增, 记, 们, 凑, 憩,\n",
      "Nearest to 深: 密, 浓, 浅, 静, 藏, 杪, 隘, 锁,\n",
      "Nearest to 尽: 了, 却, 麽, 都, 便, 贳, 捋, 冲,\n",
      "Average loss at step  242000 :  4.096516665816307\n",
      "Average loss at step  244000 :  4.1053295432329175\n",
      "Average loss at step  246000 :  4.092574970841408\n",
      "Average loss at step  248000 :  4.111995567083358\n",
      "Average loss at step  250000 :  4.1231519113779065\n",
      "Nearest to 水: 江, 溪, 渚, 滔, 波, 脊, 蕖, 堠,\n",
      "Nearest to 自: 请, 疑, 正, 便, 记, 扬, 最, 钥,\n",
      "Nearest to UNK: 秉, 赪, 闹, 瞢, 刍, 滔, 单, 庄,\n",
      "Nearest to 月: 蟾, 日, 赣, 影, 痼, 截, 灯, 展,\n",
      "Nearest to 梅: 菊, 杏, 桃, 雪, 梨, 荻, 券, 骢,\n",
      "Nearest to 楼: 城, 殿, 台, 阑, 但, 肱, 层, 望,\n",
      "Nearest to 又: 早, 已, 还, 拏, 复, 忽, 怕, 更,\n",
      "Nearest to 似: 如, 是, 记, 为, 见, 奈, 在, 与,\n",
      "Nearest to 、: ，, 。, ）, 璐, 峣, 旄, 儋, 撋,\n",
      "Nearest to 更: 便, 最, 但, 又, 共, 奈, 渐, 偏,\n",
      "Nearest to 为: 与, 借, 似, 因, 同, 共, 贫, 送,\n",
      "Nearest to 明: 皓, 光, 湛, 虺, 照, 辉, 璧, 搢,\n",
      "Nearest to 烟: 雾, 云, 驺, 涨, 霭, 冻, 骤, 疟,\n",
      "Nearest to 年: 岁, 时, 日, 记, 增, 稀, 泡, 骰,\n",
      "Nearest to 深: 密, 浅, 浓, 静, 何, 杪, 藏, 隘,\n",
      "Nearest to 尽: 却, 了, 都, 麽, 便, 共, 总, 捋,\n",
      "Average loss at step  252000 :  4.047670185923576\n",
      "Average loss at step  254000 :  4.022381420135498\n",
      "Average loss at step  256000 :  4.052458467245102\n",
      "Average loss at step  258000 :  4.002412779927254\n",
      "Average loss at step  260000 :  4.02318572640419\n",
      "Nearest to 水: 江, 溪, 渚, 滔, 脊, 山, 蕖, 堠,\n",
      "Nearest to 自: 疑, 请, 便, 正, 扬, 裛, 记, 醇,\n",
      "Nearest to UNK: 秉, 赪, 闹, 暾, 刍, 幰, 润, 瞢,\n",
      "Nearest to 月: 蟾, 赣, 日, 影, 灯, 疟, , 晦,\n",
      "Nearest to 梅: 菊, 杏, 桃, 雪, 梨, 荻, 花, 叩,\n",
      "Nearest to 楼: 城, 台, 殿, 望, 但, 阑, 肱, 层,\n",
      "Nearest to 又: 早, 已, 拏, 还, 忽, 匆, 复, 况,\n",
      "Nearest to 似: 如, 是, 为, 在, 记, 做, 与, 奈,\n",
      "Nearest to 、: ，, 。, 璐, 峣, 贰, ）, 旄, 儋,\n",
      "Nearest to 更: 便, 但, 最, 渐, 偏, 奈, 共, 又,\n",
      "Nearest to 为: 与, 似, 借, 因, 共, 同, 遣, 贫,\n",
      "Nearest to 明: 皓, 光, 湛, 照, 十, 辉, 晓, 缺,\n",
      "Nearest to 烟: 雾, 云, 香, 驺, 远, 涨, 霭, 外,\n",
      "Nearest to 年: 岁, 时, 增, 日, 记, 们, 稀, 憩,\n",
      "Nearest to 深: 密, 浓, 浅, 静, 何, 藏, 杪, /,\n",
      "Nearest to 尽: 却, 了, 麽, 共, 冲, 都, 总, 便,\n",
      "Average loss at step  262000 :  4.035967930853367\n",
      "Average loss at step  264000 :  4.026068866610527\n",
      "Average loss at step  266000 :  4.056807168960571\n",
      "Average loss at step  268000 :  4.115290866017341\n",
      "Average loss at step  270000 :  4.083555095672607\n",
      "Nearest to 水: 江, 溪, 渚, 堠, 滔, 脊, 山, 蕖,\n",
      "Nearest to 自: 请, 醇, 贞, 记, 疑, 扬, 傲, 钥,\n",
      "Nearest to UNK: 秉, 赪, 闹, 冤, 幰, 刍, 单, 陀,\n",
      "Nearest to 月: 蟾, 赣, 日, 影, 疟, 灯, 痼, 截,\n",
      "Nearest to 梅: 杏, 菊, 桃, 荻, 梨, 雪, 券, 骢,\n",
      "Nearest to 楼: 城, 阑, 但, 台, 殿, 峰, 肱, 望,\n",
      "Nearest to 又: 早, 已, 拏, 忽, 还, 复, 匆, 况,\n",
      "Nearest to 似: 如, 记, 为, 在, 做, 是, 倩, 胜,\n",
      "Nearest to 、: ，, 。, ）, 迷, 峣, 儋, 旄, 花,\n",
      "Nearest to 更: 便, 最, 但, 又, 渐, 偏, 奈, 共,\n",
      "Nearest to 为: 与, 似, 因, 同, 借, 贫, 送, 共,\n",
      "Nearest to 明: 皓, 光, 湛, 辉, 照, 搢, 杲, 虺,\n",
      "Nearest to 烟: 雾, 云, 驺, 骤, 湄, 冻, 香, 外,\n",
      "Nearest to 年: 岁, 时, 增, 日, 记, 们, 椽, 稀,\n",
      "Nearest to 深: 密, 浓, 浅, 静, 藏, 杪, 锁, 隘,\n",
      "Nearest to 尽: 却, 了, 都, 便, 总, 共, 麽, 冲,\n",
      "Average loss at step  272000 :  4.092467550396919\n",
      "Average loss at step  274000 :  4.082068603396416\n",
      "Average loss at step  276000 :  4.107860653281212\n",
      "Average loss at step  278000 :  4.111638091206551\n",
      "Average loss at step  280000 :  4.029558045387268\n",
      "Nearest to 水: 江, 溪, 山, 滔, 波, 渚, 堠, 泸,\n",
      "Nearest to 自: 请, 疑, 正, 扬, 记, 最, 应, 便,\n",
      "Nearest to UNK: 秉, 赪, 闹, 刍, 润, 郸, 冤, 婉,\n",
      "Nearest to 月: 蟾, 日, 赣, 影, 之, 截, 照, 疟,\n",
      "Nearest to 梅: 菊, 杏, 桃, 梨, 雪, 荻, 骢, 券,\n",
      "Nearest to 楼: 城, 阑, 但, 台, 殿, 望, 层, 峰,\n",
      "Nearest to 又: 早, 已, 还, 拏, 忽, 匆, 怕, 复,\n",
      "Nearest to 似: 如, 是, 记, 为, 奈, 在, 见, 又,\n",
      "Nearest to 、: ，, 。, ）, 璐, 儋, 撋, 添, 旄,\n",
      "Nearest to 更: 最, 便, 但, 共, 渐, 又, 奈, 偏,\n",
      "Nearest to 为: 与, 共, 借, 似, 因, 同, 输, 贫,\n",
      "Nearest to 明: 光, 皓, 湛, 辉, 晓, 皎, 润, 裾,\n",
      "Nearest to 烟: 雾, 云, 驺, 涨, 香, 湄, 霭, 冻,\n",
      "Nearest to 年: 岁, 时, 稀, 增, 记, 泡, 多, 老,\n",
      "Nearest to 深: 密, 浅, 浓, 静, 何, 杪, 藏, 窈,\n",
      "Nearest to 尽: 却, 了, 总, 都, 共, 麽, 便, 冲,\n",
      "Average loss at step  282000 :  4.018348894834518\n",
      "Average loss at step  284000 :  4.0335908560752864\n",
      "Average loss at step  286000 :  3.990175573825836\n",
      "Average loss at step  288000 :  4.011688160538673\n",
      "Average loss at step  290000 :  4.028523599117994\n",
      "Nearest to 水: 江, 溪, 滔, 脊, 山, 渚, 堠, 蕖,\n",
      "Nearest to 自: 疑, 请, 正, 扬, 记, 便, 讫, 醇,\n",
      "Nearest to UNK: 秉, 赪, 闹, 刍, 郸, 陀, 暾, 幰,\n",
      "Nearest to 月: 蟾, 赣, 日, 影, 灯, 疟, 汴, ,\n",
      "Nearest to 梅: 菊, 杏, 桃, 雪, 梨, 荻, 灯, 花,\n",
      "Nearest to 楼: 城, 台, 阑, 层, 肱, 望, 但, 殿,\n",
      "Nearest to 又: 早, 已, 拏, 还, 忽, 匆, 复, 况,\n",
      "Nearest to 似: 如, 是, 为, 记, 做, 在, 奈, 与,\n",
      "Nearest to 、: ，, 。, 峣, ）, 儋, 旄, 璐, 输,\n",
      "Nearest to 更: 便, 但, 最, 共, 渐, 偏, 奈, 又,\n",
      "Nearest to 为: 与, 似, 借, 因, 同, 遣, 共, 魄,\n",
      "Nearest to 明: 光, 皓, 湛, 辉, 照, 虺, 今, 十,\n",
      "Nearest to 烟: 雾, 云, 驺, 远, 湄, 霭, 骤, 涨,\n",
      "Nearest to 年: 岁, 时, 增, 日, 记, 们, 凑, 憩,\n",
      "Nearest to 深: 密, 浅, 浓, 静, 藏, 何, 期, 杪,\n",
      "Nearest to 尽: 却, 了, 麽, 冲, 都, 共, 总, 断,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  292000 :  4.021159394383431\n",
      "Average loss at step  294000 :  4.0560802681446075\n",
      "Average loss at step  296000 :  4.095091129422188\n",
      "Average loss at step  298000 :  4.071357855200768\n",
      "Average loss at step  300000 :  4.086301964044571\n",
      "Nearest to 水: 江, 渚, 溪, 滔, 堠, 脊, 蕖, 津,\n",
      "Nearest to 自: 正, 记, 疑, 扬, 应, 最, 泫, 请,\n",
      "Nearest to UNK: 秉, 赪, 闹, 刍, 冤, 幰, 润, 瞢,\n",
      "Nearest to 月: 蟾, 日, 影, 赣, 灯, 痼, 疟, 秋,\n",
      "Nearest to 梅: 杏, 菊, 桃, 荻, 梨, 雪, 骢, 柳,\n",
      "Nearest to 楼: 城, 台, 阑, 但, 肱, 望, 殿, 萏,\n",
      "Nearest to 又: 已, 早, 拏, 忽, 复, 还, 匆, 更,\n",
      "Nearest to 似: 如, 是, 记, 为, 做, 奈, 在, 倩,\n",
      "Nearest to 、: ，, 。, ）, 迷, 峣, 旄, 人, 黏,\n",
      "Nearest to 更: 最, 便, 但, 又, 共, 奈, 渐, 也,\n",
      "Nearest to 为: 与, 似, 同, 借, 共, 因, 遣, 献,\n",
      "Nearest to 明: 皓, 光, 辉, 湛, 虺, 搢, 照, 杲,\n",
      "Nearest to 烟: 雾, 云, 驺, 湄, 外, 冻, 涨, 冉,\n",
      "Nearest to 年: 岁, 时, 日, 增, 顷, 椽, 仰, 凑,\n",
      "Nearest to 深: 密, 浅, 浓, 藏, 静, 锁, 杪, 期,\n",
      "Nearest to 尽: 却, 了, 总, 共, 都, 冲, 麽, 便,\n",
      "Average loss at step  302000 :  4.061509970545769\n",
      "Average loss at step  304000 :  4.0998969532251355\n",
      "Average loss at step  306000 :  4.0992969306707385\n",
      "Average loss at step  308000 :  4.015162232756615\n",
      "Average loss at step  310000 :  4.013340380430222\n",
      "Nearest to 水: 江, 溪, 滔, 山, 波, 渚, 箭, 蕖,\n",
      "Nearest to 自: 请, 扬, 正, 疑, 最, 便, 应, 记,\n",
      "Nearest to UNK: 秉, 赪, 闹, 润, 刍, 郸, 暾, 婉,\n",
      "Nearest to 月: 蟾, 日, 赣, 影, 灯, 疟, 照, 塾,\n",
      "Nearest to 梅: 杏, 菊, 桃, 雪, 骢, 荻, 梨, 灯,\n",
      "Nearest to 楼: 城, 阑, 但, 台, 殿, 肱, 望, 层,\n",
      "Nearest to 又: 早, 已, 还, 拏, 匆, 怕, 忽, 复,\n",
      "Nearest to 似: 如, 是, 记, 为, 在, 奈, 疑, 胜,\n",
      "Nearest to 、: ，, 。, ）, 璐, 花, □, 儋, 撋,\n",
      "Nearest to 更: 便, 最, 但, 共, 奈, 又, 且, 偏,\n",
      "Nearest to 为: 与, 似, 借, 因, 同, 共, 贫, 遣,\n",
      "Nearest to 明: 光, 皓, 晓, 辉, 湛, 搢, 皎, 缺,\n",
      "Nearest to 烟: 雾, 云, 驺, 霭, 涨, 湄, 诘, 冉,\n",
      "Nearest to 年: 岁, 时, 日, 稀, 增, 记, 泡, 牝,\n",
      "Nearest to 深: 密, 浅, 浓, 静, 何, 窈, 杪, 藏,\n",
      "Nearest to 尽: 了, 却, 总, 共, 冲, 麽, 都, 便,\n",
      "Average loss at step  312000 :  4.029349360823631\n",
      "Average loss at step  314000 :  3.981152650117874\n",
      "Average loss at step  316000 :  3.994946161866188\n",
      "Average loss at step  318000 :  4.031169662952423\n",
      "Average loss at step  320000 :  4.0164690773487095\n",
      "Nearest to 水: 江, 滔, 脊, 溪, 渚, 堠, 蕖, 曦,\n",
      "Nearest to 自: 疑, 正, 便, 记, 应, 请, 醇, 最,\n",
      "Nearest to UNK: 秉, 赪, 闹, 刍, 润, 幰, 陀, 瞢,\n",
      "Nearest to 月: 蟾, 赣, 影, 日, 灯, 疟, , 晦,\n",
      "Nearest to 梅: 杏, 菊, 桃, 荻, 梨, 雪, 花, 灯,\n",
      "Nearest to 楼: 城, 阑, 台, 望, 但, 殿, 肱, 层,\n",
      "Nearest to 又: 已, 早, 还, 拏, 匆, 复, 忽, 况,\n",
      "Nearest to 似: 如, 是, 记, 为, 做, 在, 与, 奈,\n",
      "Nearest to 、: ，, 。, 峣, ）, □, 说, 花, 听,\n",
      "Nearest to 更: 便, 但, 最, 共, 渐, 又, 奈, 偏,\n",
      "Nearest to 为: 与, 似, 同, 因, 借, 共, 遣, 魄,\n",
      "Nearest to 明: 皓, 光, 湛, 照, 辉, 搢, 十, 灏,\n",
      "Nearest to 烟: 雾, 云, 驺, 远, 湄, 霭, 涨, 骤,\n",
      "Nearest to 年: 岁, 时, 增, 日, 缕, 记, 憩, 韶,\n",
      "Nearest to 深: 密, 浅, 浓, 静, 藏, 障, 期, /,\n",
      "Nearest to 尽: 了, 却, 冲, 都, 共, 麽, 便, 总,\n",
      "Average loss at step  322000 :  4.050421270132065\n",
      "Average loss at step  324000 :  4.085846159338951\n",
      "Average loss at step  326000 :  4.058737941503525\n",
      "Average loss at step  328000 :  4.073424497127533\n",
      "Average loss at step  330000 :  4.057441852211952\n",
      "Nearest to 水: 江, 滔, 渚, 溪, 堠, 津, 蕖, 脊,\n",
      "Nearest to 自: 正, 疑, 最, 扬, 记, 应, 泫, 便,\n",
      "Nearest to UNK: 秉, 赪, 冤, 闹, 刍, 幰, 瞢, 润,\n",
      "Nearest to 月: 蟾, 影, 赣, 日, 截, 雪, 灯, 缸,\n",
      "Nearest to 梅: 杏, 菊, 桃, 骢, 荻, 灯, 梨, 雪,\n",
      "Nearest to 楼: 城, 但, 阑, 台, 萏, 望, 阁, 层,\n",
      "Nearest to 又: 已, 早, 拏, 还, 忽, 匆, 复, 怕,\n",
      "Nearest to 似: 如, 记, 是, 为, 奈, 在, 胜, 倩,\n",
      "Nearest to 、: ，, 。, ）, 璐, 添, 儋, 迷, 汜,\n",
      "Nearest to 更: 最, 便, 但, 共, 又, 奈, 渐, 也,\n",
      "Nearest to 为: 与, 似, 因, 同, 借, 贫, 共, 献,\n",
      "Nearest to 明: 皓, 光, 湛, 辉, 搢, 照, 杲, 璧,\n",
      "Nearest to 烟: 雾, 云, 驺, 湄, 涨, 外, 霭, 冻,\n",
      "Nearest to 年: 岁, 时, 记, 牝, 日, 增, 泡, 韶,\n",
      "Nearest to 深: 密, 浅, 浓, 静, 杪, 藏, 何, 期,\n",
      "Nearest to 尽: 却, 了, 都, 总, 共, 便, 冲, 麽,\n",
      "Average loss at step  332000 :  4.0858838293552395\n",
      "Average loss at step  334000 :  4.09701950097084\n",
      "Average loss at step  336000 :  3.9938204954862595\n",
      "Average loss at step  338000 :  3.9983695586919783\n",
      "Average loss at step  340000 :  4.030730153560638\n",
      "Nearest to 水: 江, 滔, 渚, 溪, 蕖, 脊, 波, 山,\n",
      "Nearest to 自: 扬, 正, 疑, 便, 请, 最, 泫, 应,\n",
      "Nearest to UNK: 秉, 赪, 润, 刍, 闹, 郸, 冤, 暾,\n",
      "Nearest to 月: 蟾, 赣, 日, 影, 灯, 花, 展, 雪,\n",
      "Nearest to 梅: 菊, 杏, 桃, 荻, 梨, 雪, 灯, 骢,\n",
      "Nearest to 楼: 城, 台, 阑, 但, 望, 层, 殿, 槽,\n",
      "Nearest to 又: 早, 已, 拏, 还, 匆, 忽, 复, 怕,\n",
      "Nearest to 似: 如, 是, 为, 记, 奈, 在, 做, 见,\n",
      "Nearest to 、: ，, 。, ）, 璐, 儋, 输, 峣, 入,\n",
      "Nearest to 更: 便, 最, 但, 共, 奈, 又, 渐, 已,\n",
      "Nearest to 为: 与, 似, 因, 借, 同, 共, 贫, 倩,\n",
      "Nearest to 明: 光, 皓, 晓, 搢, 辉, 缺, 湛, 皎,\n",
      "Nearest to 烟: 雾, 云, 驺, 湄, 霭, 外, 远, 诘,\n",
      "Nearest to 年: 岁, 时, 增, 日, 稀, 泡, 韶, 老,\n",
      "Nearest to 深: 密, 静, 浓, 浅, 何, 杪, 藏, 寂,\n",
      "Nearest to 尽: 了, 却, 冲, 总, 共, 麽, 半, 断,\n",
      "Average loss at step  342000 :  3.964141118764877\n",
      "Average loss at step  344000 :  3.9821924982666967\n",
      "Average loss at step  346000 :  4.026205457568168\n",
      "Average loss at step  348000 :  4.006438010215759\n",
      "Average loss at step  350000 :  4.048674818038941\n",
      "Nearest to 水: 江, 滔, 溪, 渚, 堠, 蕖, 脊, 山,\n",
      "Nearest to 自: 正, 疑, 请, 扬, 钥, 记, 醇, 便,\n",
      "Nearest to UNK: 秉, 赪, 润, 刍, 冤, 闹, 幰, 陀,\n",
      "Nearest to 月: 蟾, 赣, 日, 灯, 影, 疟, 秋, 刷,\n",
      "Nearest to 梅: 杏, 菊, 桃, 荻, 花, 梨, 雪, 柳,\n",
      "Nearest to 楼: 城, 阑, 望, 台, 但, 萏, 肱, 梅,\n",
      "Nearest to 又: 早, 已, 还, 拏, 匆, 复, 忽, 更,\n",
      "Nearest to 似: 如, 是, 为, 记, 做, 与, 在, 倩,\n",
      "Nearest to 、: ，, 。, ）, 峣, 擞, 儋, 添, 璐,\n",
      "Nearest to 更: 便, 但, 最, 又, 共, 渐, 奈, 已,\n",
      "Nearest to 为: 与, 同, 似, 因, 共, 借, 魄, 遣,\n",
      "Nearest to 明: 光, 皓, 辉, 灏, 虺, 湛, 照, 今,\n",
      "Nearest to 烟: 雾, 云, 霭, 驺, 湄, 远, 外, 骤,\n",
      "Nearest to 年: 岁, 时, 日, 增, 们, 老, 记, 稀,\n",
      "Nearest to 深: 密, 浓, 浅, 静, 期, 杪, 何, 藏,\n",
      "Nearest to 尽: 了, 却, 冲, 都, 共, 便, 麽, 总,\n",
      "Average loss at step  352000 :  4.071622282981872\n",
      "Average loss at step  354000 :  4.053373377680779\n",
      "Average loss at step  356000 :  4.066226876139641\n",
      "Average loss at step  358000 :  4.049166787385941\n",
      "Average loss at step  360000 :  4.079792213797569\n",
      "Nearest to 水: 江, 滔, 溪, 堠, 渚, 脊, 山, 津,\n",
      "Nearest to 自: 正, 请, 最, 疑, 扬, 醇, 应, 泫,\n",
      "Nearest to UNK: 秉, 赪, 刍, 冤, 瞢, 恻, 幰, 闹,\n",
      "Nearest to 月: 蟾, 赣, 日, 影, 截, 缸, 雪, 展,\n",
      "Nearest to 梅: 杏, 菊, 桃, 雪, 荻, 梨, 骢, 灯,\n",
      "Nearest to 楼: 城, 阑, 台, 但, 望, 层, 阁, 梅,\n",
      "Nearest to 又: 早, 已, 复, 拏, 匆, 还, 怕, 忽,\n",
      "Nearest to 似: 如, 是, 记, 为, 奈, 倩, 见, 与,\n",
      "Nearest to 、: ，, 。, ）, 璐, 说, 儋, 载, 于,\n",
      "Nearest to 更: 最, 便, 但, 又, 共, 奈, 渐, 亦,\n",
      "Nearest to 为: 与, 似, 同, 借, 因, 共, 魄, 遣,\n",
      "Nearest to 明: 皓, 光, 辉, 搢, 虺, 杲, 湛, 玷,\n",
      "Nearest to 烟: 雾, 云, 霭, 驺, 外, 湄, 幂, 骤,\n",
      "Nearest to 年: 岁, 时, 日, 增, 牝, 稀, 泡, 讼,\n",
      "Nearest to 深: 浅, 密, 浓, 静, 杪, 何, /, 锁,\n",
      "Nearest to 尽: 了, 却, 总, 都, 共, 便, 麽, 冲,\n",
      "Average loss at step  362000 :  4.087785005807876\n",
      "Average loss at step  364000 :  3.9860172213315965\n",
      "Average loss at step  366000 :  3.988300724744797\n",
      "Average loss at step  368000 :  4.027048087954521\n",
      "Average loss at step  370000 :  3.9618670415878294\n",
      "Nearest to 水: 江, 滔, 溪, 渚, 山, 脊, 堠, 波,\n",
      "Nearest to 自: 疑, 正, 泫, 扬, 裛, 讫, 便, 请,\n",
      "Nearest to UNK: 秉, 赪, 闹, 刍, 润, 冤, 郸, 暾,\n",
      "Nearest to 月: 蟾, 日, 赣, 影, 灯, 截, 疟, 晦,\n",
      "Nearest to 梅: 杏, 菊, 桃, 梨, 荻, 花, 雪, 灯,\n",
      "Nearest to 楼: 城, 台, 阑, 但, 望, 阁, 层, 肱,\n",
      "Nearest to 又: 已, 早, 拏, 匆, 还, 复, 忽, 怕,\n",
      "Nearest to 似: 如, 是, 为, 记, 奈, 在, 做, 与,\n",
      "Nearest to 、: ，, 。, 璐, ）, 撋, 输, 峣, ；,\n",
      "Nearest to 更: 最, 便, 但, 共, 奈, 渐, 又, 处,\n",
      "Nearest to 为: 与, 似, 因, 同, 借, 共, 遣, 羡,\n",
      "Nearest to 明: 皓, 光, 晓, 照, 灏, 玷, 搢, 缺,\n",
      "Nearest to 烟: 雾, 云, 驺, 霭, 香, 湄, 远, 外,\n",
      "Nearest to 年: 岁, 时, 增, 日, 泡, 韶, 牝, 们,\n",
      "Nearest to 深: 密, 浓, 浅, 静, 何, /, 藏, 杪,\n",
      "Nearest to 尽: 了, 却, 共, 冲, 总, 便, 麽, 捋,\n",
      "Average loss at step  372000 :  3.9756896391510965\n",
      "Average loss at step  374000 :  4.0132522073984145\n",
      "Average loss at step  376000 :  3.9977188390493392\n",
      "Average loss at step  378000 :  4.043966340780258\n",
      "Average loss at step  380000 :  4.060501255750657\n",
      "Nearest to 水: 江, 滔, 渚, 堠, 溪, 脊, 山, 浦,\n",
      "Nearest to 自: 正, 贞, 钥, 疑, 应, 醇, 扬, 最,\n",
      "Nearest to UNK: 秉, 赪, 冤, 刍, 闹, 幰, 润, 辱,\n",
      "Nearest to 月: 蟾, 赣, 影, 日, 秋, 截, 疟, 痼,\n",
      "Nearest to 梅: 杏, 菊, 桃, 荻, 梨, 灯, 柳, 楼,\n",
      "Nearest to 楼: 阑, 城, 望, 台, 但, 阁, 峰, 萏,\n",
      "Nearest to 又: 早, 已, 拏, 复, 匆, 还, 怕, 更,\n",
      "Nearest to 似: 如, 是, 为, 记, 做, 胜, 在, 与,\n",
      "Nearest to 、: ，, 。, ）, 皆, 片, 儋, 汜, 花,\n",
      "Nearest to 更: 便, 最, 但, 又, 共, 奈, 渐, 惋,\n",
      "Nearest to 为: 与, 似, 同, 魄, 赠, 借, 因, 献,\n",
      "Nearest to 明: 灏, 辉, 皓, 光, 湛, 玷, 搢, 杲,\n",
      "Nearest to 烟: 雾, 云, 霭, 湄, 驺, 骤, 冉, 幂,\n",
      "Nearest to 年: 岁, 时, 日, 增, 们, 椽, 牝, 缕,\n",
      "Nearest to 深: 浓, 密, 浅, 静, 杪, 啼, 锁, /,\n",
      "Nearest to 尽: 了, 却, 冲, 共, 总, 便, 都, 遍,\n",
      "Average loss at step  382000 :  4.045539276957512\n",
      "Average loss at step  384000 :  4.057229180693627\n",
      "Average loss at step  386000 :  4.049539440274239\n",
      "Average loss at step  388000 :  4.0686523414850235\n",
      "Average loss at step  390000 :  4.075698006033897\n",
      "Nearest to 水: 江, 溪, 滔, 脊, 波, 渚, 堠, 泸,\n",
      "Nearest to 自: 正, 最, 请, 疑, 泫, 应, 定, 钥,\n",
      "Nearest to UNK: 秉, 赪, 刍, 润, 郸, 恻, 冤, 婉,\n",
      "Nearest to 月: 蟾, 日, 赣, 影, 截, 雪, 爨, 缸,\n",
      "Nearest to 梅: 杏, 菊, 桃, 梨, 雪, 荻, 灯, 骢,\n",
      "Nearest to 楼: 城, 阑, 台, 望, 但, 层, 槽, 肱,\n",
      "Nearest to 又: 已, 早, 拏, 还, 怕, 匆, 复, 更,\n",
      "Nearest to 似: 如, 是, 奈, 为, 记, 与, 见, 在,\n",
      "Nearest to 、: ，, 。, ）, 儋, 璐, 去, 撋, 于,\n",
      "Nearest to 更: 最, 便, 但, 共, 又, 奈, 亦, 也,\n",
      "Nearest to 为: 与, 似, 同, 借, 因, 共, 赠, 送,\n",
      "Nearest to 明: 光, 皓, 搢, 辉, 湛, 皎, 照, 晓,\n",
      "Nearest to 烟: 雾, 云, 霭, 驺, 幂, 岚, 冉, 湄,\n",
      "Nearest to 年: 岁, 时, 日, 稀, 牝, 韶, 缕, 常,\n",
      "Nearest to 深: 浓, 密, 静, 浅, 窈, 杪, 何, /,\n",
      "Nearest to 尽: 了, 却, 总, 共, 都, 便, 取, 麽,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  392000 :  3.971375120997429\n",
      "Average loss at step  394000 :  3.9879282091856\n",
      "Average loss at step  396000 :  4.014280008435249\n",
      "Average loss at step  398000 :  3.958713647246361\n"
     ]
    }
   ],
   "source": [
    "num_steps = 400000\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "  # We must initialize all variables before we use them.\n",
    "  init.run()\n",
    "  print('Initialized')\n",
    "\n",
    "  average_loss = 0\n",
    "  for step in xrange(num_steps):\n",
    "    batch_inputs, batch_labels = generate_batch(\n",
    "        batch_size, num_skips, skip_window)\n",
    "    feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}\n",
    "\n",
    "    # We perform one update step by evaluating the optimizer op (including it\n",
    "    # in the list of returned values for session.run()\n",
    "    _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)\n",
    "    average_loss += loss_val\n",
    "\n",
    "    if step % 2000 == 0:\n",
    "      if step > 0:\n",
    "        average_loss /= 2000\n",
    "      # The average loss is an estimate of the loss over the last 2000 batches.\n",
    "      print('Average loss at step ', step, ': ', average_loss)\n",
    "      average_loss = 0\n",
    "\n",
    "    # Note that this is expensive (~20% slowdown if computed every 500 steps)\n",
    "    if step % 10000 == 0:\n",
    "      sim = similarity.eval()\n",
    "      for i in xrange(valid_size):\n",
    "        valid_word = reverse_dictionary[valid_examples[i]]\n",
    "        top_k = 8  # number of nearest neighbors\n",
    "        nearest = (-sim[i, :]).argsort()[1:top_k + 1]\n",
    "        log_str = 'Nearest to %s:' % valid_word\n",
    "        for k in xrange(top_k):\n",
    "          close_word = reverse_dictionary[nearest[k]]\n",
    "          log_str = '%s %s,' % (log_str, close_word)\n",
    "        print(log_str)\n",
    "  final_embeddings = normalized_embeddings.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### step6 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 6: Visualize the embeddings.\n",
    "\n",
    "\n",
    "# pylint: disable=missing-docstring\n",
    "# Function to draw visualization of distance between embeddings.\n",
    "def plot_with_labels(low_dim_embs, labels, filename):\n",
    "  assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'\n",
    "  plt.figure(figsize=(18, 18))  # in inches\n",
    "  for i, label in enumerate(labels):\n",
    "    x, y = low_dim_embs[i, :]\n",
    "    plt.scatter(x, y)\n",
    "    plt.annotate(label,\n",
    "                 xy=(x, y),\n",
    "                 xytext=(5, 2),\n",
    "                 textcoords='offset points',\n",
    "                 ha='right',\n",
    "                 va='bottom')\n",
    "\n",
    "  plt.savefig(filename)\n",
    "\n",
    "try:\n",
    "  # pylint: disable=g-import-not-at-top\n",
    "  from sklearn.manifold import TSNE\n",
    "  import matplotlib.pyplot as plt\n",
    "  #设置matplotlib输出中文\n",
    "  plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "  plt.rcParams['axes.unicode_minus'] = False\n",
    "  #tsne 通过pca将数据降成二维 方便在图形中显示\n",
    "  tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')\n",
    "  plot_only = 500\n",
    "  low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])\n",
    "  labels = [reverse_dictionary[i] for i in xrange(plot_only)]\n",
    "  plot_with_labels(low_dim_embs, labels, os.path.join(os.getcwd(), 'tsne.png'))\n",
    "\n",
    "except ImportError as ex:\n",
    "  print('Please install sklearn, matplotlib, and scipy to show embeddings.')\n",
    "  print(ex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### step7  通过json方式保存dictionary和reversed_dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88692"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "#将计算得到的enmbeding 权重参数保存\n",
    "np.save('embedding.npy', final_embeddings)\n",
    "\n",
    "f_dic = open('dictionary.json','w')\n",
    "f_dic.write(json.dumps(dictionary))\n",
    "\n",
    "f_redic=open('reversed_dictionary.json','w')\n",
    "f_redic.write(json.dumps(reverse_dictionary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
